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Semi-supervised multi-target prediction for analysis of screening data
Published on Jun 28, 201976 Views
The predictive performance of traditional supervised methods heavily depends on the amount of labeled data. However, obtaining labels is a difficult process in many real-life tasks including compound
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Chapter list
Semi-supervised multi-target prediction for analysis of screening data00:00
What is semi-supervised learning?00:34
Why semi-supervised learning?02:06
Outline - 103:08
The task of semi-supervised learning03:32
How unlabeled data can help?05:12
Why multi-target prediction?07:40
SSL for classification tasks10:44
SSL for regression tasks11:18
SSL for multi-label classification11:45
SSL for multi-target regression11:57
Existing SSL methods for SOP12:11
Limitations of the existing methods12:42
Outline - 213:43
Predictive clustering trees13:54
Supervised PCTs14:29
PCTs instantiations15:17
Semi-supervised PCTs16:06
Predictive clustering17:42
Ensembles of semi-supervised PCTs18:51
Outline - 319:20
Experimental evaluation19:24
Experimental setup20:26
Predictive performance (examples)21:07
Statistical analysis22:12
Influence of the w parameter22:53
Influence of the unlabeled data23:57
Interpretability and model sizes24:47
SSL-PCTs for primitive outputs25:47
Illustrative study on QSAR datasets26:26
Performance results26:57
Interpretability potential27:28
Obtained PCTs - 128:27
Obtained PCTs - 229:02
Obtained PCTs - 329:09
Obtained PCTs - 429:14
Obtained PCTs - 529:17
Outline - 429:22
Conclusions29:25